[BUG] An error occurred (ValidationException) when calling the Converse operation: This model doesn't support the toolConfig.toolChoice.any field. Remove toolConfig.toolChoice.any and try again #1241
Description
Checks
- I have updated to the lastest minor and patch version of Strands
- I have checked the documentation and this is not expected behavior
- I have searched ./issues and there are no duplicates of my issue
Strands Version
1.18.0
Python Version
3.13
Operating System
macOS
Installation Method
pip
Steps to Reproduce
- Use structured output model
- Use Llama models using BedrockModel class
- When the model doesn't return the output in structured format for the first time, this error is thrown
Expected Behavior
Validation error should not be thrown in case of structured output
Actual Behavior
/Users/ajay/Documents/GitHub/azaylamba/strands-sdk-python/tests_integ/test_structured_output_bedrock_llama_models.py::TestBedrockLlamaModelsToolUsageWithStructuredOutput::test_multi_turn_calculator_tool_use_with_structured_output failed: agent = <strands.agent.agent.Agent object at 0x1095882f0>
invocation_state = {'agent': <strands.agent.agent.Agent object at 0x1095882f0>, 'event_loop_cycle_id': UUID('3a436be6-4599-4bf4-8f3a-d525...ce_state=[], is_remote=False)), 'event_loop_cycle_trace': <strands.telemetry.metrics.Trace object at 0x108de5fd0>, ...}
structured_output_context = <strands.tools.structured_output._structured_output_context.StructuredOutputContext object at 0x10958a270>
async def event_loop_cycle(
agent: "Agent",
invocation_state: dict[str, Any],
structured_output_context: StructuredOutputContext | None = None,
) -> AsyncGenerator[TypedEvent, None]:
"""Execute a single cycle of the event loop.
This core function processes a single conversation turn, handling model inference, tool execution, and error
recovery. It manages the entire lifecycle of a conversation turn, including:
1. Initializing cycle state and metrics
2. Checking execution limits
3. Processing messages with the model
4. Handling tool execution requests
5. Managing recursive calls for multi-turn tool interactions
6. Collecting and reporting metrics
7. Error handling and recovery
Args:
agent: The agent for which the cycle is being executed.
invocation_state: Additional arguments including:
- request_state: State maintained across cycles
- event_loop_cycle_id: Unique ID for this cycle
- event_loop_cycle_span: Current tracing Span for this cycle
structured_output_context: Optional context for structured output management.
Yields:
Model and tool stream events. The last event is a tuple containing:
- StopReason: Reason the model stopped generating (e.g., "tool_use")
- Message: The generated message from the model
- EventLoopMetrics: Updated metrics for the event loop
- Any: Updated request state
Raises:
EventLoopException: If an error occurs during execution
ContextWindowOverflowException: If the input is too large for the model
"""
structured_output_context = structured_output_context or StructuredOutputContext()
# Initialize cycle state
invocation_state["event_loop_cycle_id"] = uuid.uuid4()
# Initialize state and get cycle trace
if "request_state" not in invocation_state:
invocation_state["request_state"] = {}
attributes = {"event_loop_cycle_id": str(invocation_state.get("event_loop_cycle_id"))}
cycle_start_time, cycle_trace = agent.event_loop_metrics.start_cycle(attributes=attributes)
invocation_state["event_loop_cycle_trace"] = cycle_trace
yield StartEvent()
yield StartEventLoopEvent()
# Create tracer span for this event loop cycle
tracer = get_tracer()
cycle_span = tracer.start_event_loop_cycle_span(
invocation_state=invocation_state, messages=agent.messages, parent_span=agent.trace_span
)
invocation_state["event_loop_cycle_span"] = cycle_span
# Skipping model invocation if in interrupt state as interrupts are currently only supported for tool calls.
if agent._interrupt_state.activated:
stop_reason: StopReason = "tool_use"
message = agent._interrupt_state.context["tool_use_message"]
# Skip model invocation if the latest message contains ToolUse
elif _has_tool_use_in_latest_message(agent.messages):
stop_reason = "tool_use"
message = agent.messages[-1]
else:
model_events = _handle_model_execution(
agent, cycle_span, cycle_trace, invocation_state, tracer, structured_output_context
)
async for model_event in model_events:
if not isinstance(model_event, ModelStopReason):
yield model_event
stop_reason, message, *_ = model_event["stop"]
yield ModelMessageEvent(message=message)
try:
if stop_reason == "max_tokens":
"""
Handle max_tokens limit reached by the model.
When the model reaches its maximum token limit, this represents a potentially unrecoverable
state where the model's response was truncated. By default, Strands fails hard with an
MaxTokensReachedException to maintain consistency with other failure types.
"""
raise MaxTokensReachedException(
message=(
"Agent has reached an unrecoverable state due to max_tokens limit. "
"For more information see: "
"https://strandsagents.com/latest/user-guide/concepts/agents/agent-loop/#maxtokensreachedexception"
)
)
if stop_reason == "tool_use":
# Handle tool execution
tool_events = _handle_tool_execution(
stop_reason,
message,
agent=agent,
cycle_trace=cycle_trace,
cycle_span=cycle_span,
cycle_start_time=cycle_start_time,
invocation_state=invocation_state,
tracer=tracer,
structured_output_context=structured_output_context,
)
> async for tool_event in tool_events:
src/strands/event_loop/event_loop.py:189:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src/strands/event_loop/event_loop.py:532: in _handle_tool_execution
async for event in events:
src/strands/event_loop/event_loop.py:277: in recurse_event_loop
async for event in events:
src/strands/event_loop/event_loop.py:237: in event_loop_cycle
async for typed_event in events:
src/strands/event_loop/event_loop.py:277: in recurse_event_loop
async for event in events:
src/strands/event_loop/event_loop.py:152: in event_loop_cycle
async for model_event in model_events:
src/strands/event_loop/event_loop.py:396: in _handle_model_execution
raise e
src/strands/event_loop/event_loop.py:337: in _handle_model_execution
async for event in stream_messages(
src/strands/event_loop/streaming.py:457: in stream_messages
async for event in process_stream(chunks, start_time):
src/strands/event_loop/streaming.py:391: in process_stream
async for chunk in chunks:
src/strands/models/bedrock.py:665: in stream
await task
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/asyncio/threads.py:25: in to_thread
return await loop.run_in_executor(None, func_call)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/concurrent/futures/thread.py:59: in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/Users/ajay/Library/Application Support/hatch/env/virtual/strands-agents/OioNP-Ga/strands-agents/lib/python3.13/site-packages/opentelemetry/instrumentation/threading/__init__.py:171: in wrapped_func
return original_func(*func_args, **func_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/strands/models/bedrock.py:786: in _stream
raise e
src/strands/models/bedrock.py:735: in _stream
response = self.client.converse(**request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/Users/ajay/Library/Application Support/hatch/env/virtual/strands-agents/OioNP-Ga/strands-agents/lib/python3.13/site-packages/botocore/client.py:602: in _api_call
return self._make_api_call(operation_name, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/Users/ajay/Library/Application Support/hatch/env/virtual/strands-agents/OioNP-Ga/strands-agents/lib/python3.13/site-packages/botocore/context.py:123: in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <botocore.client.BedrockRuntime object at 0x1095881a0>
operation_name = 'Converse'
api_params = {'inferenceConfig': {'maxTokens': 2048}, 'messages': [{'content': [{'text': 'Calculate 2 + 2 using the calculator tool...":2,"b":2}}.'}], 'role': 'assistant'}, ...], 'modelId': 'us.meta.llama4-maverick-17b-instruct-v1:0', 'system': [], ...}
@with_current_context()
def _make_api_call(self, operation_name, api_params):
operation_model = self._service_model.operation_model(operation_name)
service_name = self._service_model.service_name
history_recorder.record(
'API_CALL',
{
'service': service_name,
'operation': operation_name,
'params': api_params,
},
)
if operation_model.deprecated:
logger.debug(
'Warning: %s.%s() is deprecated', service_name, operation_name
)
request_context = {
'client_region': self.meta.region_name,
'client_config': self.meta.config,
'has_streaming_input': operation_model.has_streaming_input,
'auth_type': operation_model.resolved_auth_type,
'unsigned_payload': operation_model.unsigned_payload,
'auth_options': self._service_model.metadata.get('auth'),
}
api_params = self._emit_api_params(
api_params=api_params,
operation_model=operation_model,
context=request_context,
)
(
endpoint_url,
additional_headers,
properties,
) = self._resolve_endpoint_ruleset(
operation_model, api_params, request_context
)
if properties:
# Pass arbitrary endpoint info with the Request
# for use during construction.
request_context['endpoint_properties'] = properties
request_dict = self._convert_to_request_dict(
api_params=api_params,
operation_model=operation_model,
endpoint_url=endpoint_url,
context=request_context,
headers=additional_headers,
)
resolve_checksum_context(request_dict, operation_model, api_params)
service_id = self._service_model.service_id.hyphenize()
handler, event_response = self.meta.events.emit_until_response(
f'before-call.{service_id}.{operation_name}',
model=operation_model,
params=request_dict,
request_signer=self._request_signer,
context=request_context,
)
if event_response is not None:
http, parsed_response = event_response
else:
maybe_compress_request(
self.meta.config, request_dict, operation_model
)
apply_request_checksum(request_dict)
http, parsed_response = self._make_request(
operation_model, request_dict, request_context
)
self.meta.events.emit(
f'after-call.{service_id}.{operation_name}',
http_response=http,
parsed=parsed_response,
model=operation_model,
context=request_context,
)
if http.status_code >= 300:
error_info = parsed_response.get("Error", {})
error_code = request_context.get(
'error_code_override'
) or error_info.get("Code")
error_class = self.exceptions.from_code(error_code)
> raise error_class(parsed_response, operation_name)
E botocore.errorfactory.ValidationException: An error occurred (ValidationException) when calling the Converse operation: This model doesn't support the toolConfig.toolChoice.any field. Remove toolConfig.toolChoice.any and try again.
E └ Bedrock region: us-east-1
E └ Model id: us.meta.llama4-maverick-17b-instruct-v1:0
/Users/ajay/Library/Application Support/hatch/env/virtual/strands-agents/OioNP-Ga/strands-agents/lib/python3.13/site-packages/botocore/client.py:1078: ValidationException
The above exception was the direct cause of the following exception:
self = <tests_integ.test_structured_output_bedrock_llama_models.TestBedrockLlamaModelsToolUsageWithStructuredOutput object at 0x108dd8550>
def test_multi_turn_calculator_tool_use_with_structured_output(self):
"""Test tool usage with structured output."""
model = BedrockModel(
model_id="us.meta.llama4-maverick-17b-instruct-v1:0",
region_name="us-east-1",
max_tokens=2048,
streaming=False,
)
agent = Agent(model=model, tools=[calculator])
> result = agent("Calculate 2 + 2 using the calculator tool", structured_output_model=MathResult)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
tests_integ/test_structured_output_bedrock_llama_models.py:55:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
src/strands/agent/agent.py:349: in __call__
return run_async(
src/strands/_async.py:33: in run_async
return future.result()
^^^^^^^^^^^^^^^
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/concurrent/futures/_base.py:456: in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/concurrent/futures/_base.py:401: in __get_result
raise self._exception
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/concurrent/futures/thread.py:59: in run
result = self.fn(*self.args, **self.kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/Users/ajay/Library/Application Support/hatch/env/virtual/strands-agents/OioNP-Ga/strands-agents/lib/python3.13/site-packages/opentelemetry/instrumentation/threading/__init__.py:171: in wrapped_func
return original_func(*func_args, **func_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
src/strands/_async.py:28: in execute
return asyncio.run(execute_async())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/asyncio/runners.py:195: in run
return runner.run(main)
^^^^^^^^^^^^^^^^
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/asyncio/runners.py:118: in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
/Library/Frameworks/Python.framework/Versions/3.13/lib/python3.13/asyncio/base_events.py:719: in run_until_complete
return future.result()
^^^^^^^^^^^^^^^
src/strands/_async.py:25: in execute_async
return await async_func()
^^^^^^^^^^^^^^^^^^
src/strands/agent/agent.py:392: in invoke_async
async for event in events:
src/strands/agent/agent.py:588: in stream_async
async for event in events:
src/strands/agent/agent.py:636: in _run_loop
async for event in events:
src/strands/agent/agent.py:684: in _execute_event_loop_cycle
async for event in events:
src/strands/event_loop/event_loop.py:189: in event_loop_cycle
async for tool_event in tool_events:
src/strands/event_loop/event_loop.py:532: in _handle_tool_execution
async for event in events:
src/strands/event_loop/event_loop.py:277: in recurse_event_loop
async for event in events:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
agent = <strands.agent.agent.Agent object at 0x1095882f0>
invocation_state = {'agent': <strands.agent.agent.Agent object at 0x1095882f0>, 'event_loop_cycle_id': UUID('3a436be6-4599-4bf4-8f3a-d525...ce_state=[], is_remote=False)), 'event_loop_cycle_trace': <strands.telemetry.metrics.Trace object at 0x108de5fd0>, ...}
structured_output_context = <strands.tools.structured_output._structured_output_context.StructuredOutputContext object at 0x10958a270>
async def event_loop_cycle(
agent: "Agent",
invocation_state: dict[str, Any],
structured_output_context: StructuredOutputContext | None = None,
) -> AsyncGenerator[TypedEvent, None]:
"""Execute a single cycle of the event loop.
This core function processes a single conversation turn, handling model inference, tool execution, and error
recovery. It manages the entire lifecycle of a conversation turn, including:
1. Initializing cycle state and metrics
2. Checking execution limits
3. Processing messages with the model
4. Handling tool execution requests
5. Managing recursive calls for multi-turn tool interactions
6. Collecting and reporting metrics
7. Error handling and recovery
Args:
agent: The agent for which the cycle is being executed.
invocation_state: Additional arguments including:
- request_state: State maintained across cycles
- event_loop_cycle_id: Unique ID for this cycle
- event_loop_cycle_span: Current tracing Span for this cycle
structured_output_context: Optional context for structured output management.
Yields:
Model and tool stream events. The last event is a tuple containing:
- StopReason: Reason the model stopped generating (e.g., "tool_use")
- Message: The generated message from the model
- EventLoopMetrics: Updated metrics for the event loop
- Any: Updated request state
Raises:
EventLoopException: If an error occurs during execution
ContextWindowOverflowException: If the input is too large for the model
"""
structured_output_context = structured_output_context or StructuredOutputContext()
# Initialize cycle state
invocation_state["event_loop_cycle_id"] = uuid.uuid4()
# Initialize state and get cycle trace
if "request_state" not in invocation_state:
invocation_state["request_state"] = {}
attributes = {"event_loop_cycle_id": str(invocation_state.get("event_loop_cycle_id"))}
cycle_start_time, cycle_trace = agent.event_loop_metrics.start_cycle(attributes=attributes)
invocation_state["event_loop_cycle_trace"] = cycle_trace
yield StartEvent()
yield StartEventLoopEvent()
# Create tracer span for this event loop cycle
tracer = get_tracer()
cycle_span = tracer.start_event_loop_cycle_span(
invocation_state=invocation_state, messages=agent.messages, parent_span=agent.trace_span
)
invocation_state["event_loop_cycle_span"] = cycle_span
# Skipping model invocation if in interrupt state as interrupts are currently only supported for tool calls.
if agent._interrupt_state.activated:
stop_reason: StopReason = "tool_use"
message = agent._interrupt_state.context["tool_use_message"]
# Skip model invocation if the latest message contains ToolUse
elif _has_tool_use_in_latest_message(agent.messages):
stop_reason = "tool_use"
message = agent.messages[-1]
else:
model_events = _handle_model_execution(
agent, cycle_span, cycle_trace, invocation_state, tracer, structured_output_context
)
async for model_event in model_events:
if not isinstance(model_event, ModelStopReason):
yield model_event
stop_reason, message, *_ = model_event["stop"]
yield ModelMessageEvent(message=message)
try:
if stop_reason == "max_tokens":
"""
Handle max_tokens limit reached by the model.
When the model reaches its maximum token limit, this represents a potentially unrecoverable
state where the model's response was truncated. By default, Strands fails hard with an
MaxTokensReachedException to maintain consistency with other failure types.
"""
raise MaxTokensReachedException(
message=(
"Agent has reached an unrecoverable state due to max_tokens limit. "
"For more information see: "
"https://strandsagents.com/latest/user-guide/concepts/agents/agent-loop/#maxtokensreachedexception"
)
)
if stop_reason == "tool_use":
# Handle tool execution
tool_events = _handle_tool_execution(
stop_reason,
message,
agent=agent,
cycle_trace=cycle_trace,
cycle_span=cycle_span,
cycle_start_time=cycle_start_time,
invocation_state=invocation_state,
tracer=tracer,
structured_output_context=structured_output_context,
)
async for tool_event in tool_events:
yield tool_event
return
# End the cycle and return results
agent.event_loop_metrics.end_cycle(cycle_start_time, cycle_trace, attributes)
if cycle_span:
tracer.end_event_loop_cycle_span(
span=cycle_span,
message=message,
)
except EventLoopException as e:
if cycle_span:
tracer.end_span_with_error(cycle_span, str(e), e)
# Don't yield or log the exception - we already did it when we
# raised the exception and we don't need that duplication.
raise
except (ContextWindowOverflowException, MaxTokensReachedException) as e:
# Special cased exceptions which we want to bubble up rather than get wrapped in an EventLoopException
if cycle_span:
tracer.end_span_with_error(cycle_span, str(e), e)
raise e
except Exception as e:
if cycle_span:
tracer.end_span_with_error(cycle_span, str(e), e)
# Handle any other exceptions
yield ForceStopEvent(reason=e)
logger.exception("cycle failed")
> raise EventLoopException(e, invocation_state["request_state"]) from e
E strands.types.exceptions.EventLoopException: An error occurred (ValidationException) when calling the Converse operation: This model doesn't support the toolConfig.toolChoice.any field. Remove toolConfig.toolChoice.any and try again.
src/strands/event_loop/event_loop.py:220: EventLoopException
Additional Context
The following integration test fails intermittently as the structured output model behavior is dependent on model output.
"""
Comprehensive integration tests for structured output passed into the agent functionality.
"""
from pydantic import BaseModel, Field
from strands import Agent
from strands.models.bedrock import BedrockModel
from strands.tools import tool
class MathResult(BaseModel):
"""Math operation result."""
operation: str = Field(description="the performed operation")
result: int = Field(description="the result of the operation")
# ========== Tool Definitions ==========
@tool
def calculator(operation: str, a: float, b: float) -> float:
"""Simple calculator tool for testing."""
if operation == "add":
return a + b
elif operation == "subtract":
return a - b
elif operation == "multiply":
return a * b
elif operation == "divide":
return b / a if a != 0 else 0
elif operation == "power":
return a**b
else:
return 0
# ========== Test Classes ==========
class TestBedrockLlamaModelsToolUsageWithStructuredOutput:
"""Test structured output with tool usage."""
def test_multi_turn_calculator_tool_use_with_structured_output(self):
"""Test tool usage with structured output."""
model = BedrockModel(
model_id="us.meta.llama4-maverick-17b-instruct-v1:0",
region_name="us-east-1",
max_tokens=2048,
streaming=False,
)
agent = Agent(model=model, tools=[calculator])
result = agent("Calculate 2 + 2 using the calculator tool", structured_output_model=MathResult)
assert result.structured_output is not None
assert isinstance(result.structured_output, MathResult)
assert result.structured_output.result == 4
# Check that tool was called
assert result.metrics.tool_metrics is not None
assert len(result.metrics.tool_metrics) > 0
result = agent("What is 5 multiplied by 3? Use the calculator tool.", structured_output_model=MathResult)
assert result.structured_output is not None
Possible Solution
No response
Related Issues
No response